SoroushVahidi/scidocs huggingface dataset:
Computer vision technology for food quality assurance Sundaram Gunasekaran
(2004)

Quality assurance is one of the most important goals of any industry. The ability to manufacture high-quality products consistently is the basis for success in the highly competitive food industry. It encourages loyalty in customers and results in an expanding market share. The quality assurance methods used in the food industry have traditionally involved human visual inspection. Such methods are tedious, laborious, time-consuming and inconsistent. As plant throughput increased and quality tolerance tightened, it became necessary to employ automatic methods for quality assurance and quality control. During the past decade, computers have been introduced widely for automation of the quality assurance task. The food industry has been traditionally slow in adopting this new technology and moving towards programmable automation’. Renard? cited the following barriers to automating food processing plants: diverging opinions of what to measure because manufacturing processes themselves are not well understood; lack of appropriate standards against which properties can be compared; and the age of plants and equipment. However, the speed, reliability and cost savings inherent to process automation have provided sufficient impetus to the food industry to pursue aggressively the automatic, on-line control of all plant operations. Innovation of new technology often undergoes four phases3. The first is a research or discovery phase, in which new knowledge results in advances in the technology. Phase two is the early commercialization phase, in which a small market for the technology develops. Phase three represents the emergence of niche-specific applications of the technology. The last phase is that of widespread proliferation. A key characteristic of phase four is that the technology is transparent to users, that is,
Partial entity structure: a compact non-manifold boundary representation based on partial topological entities
(2001)

Non-manifold boundary representations have gained a great deal of popularity in recent years and various representation schemes have been proposed because they allow an even wider range of objects for various applications than conventional manifold representations. However, since these schemes are mainly interested in describing sufficient adjacency relationships of topological entities, the models represented in these schemes occupy too much storage space redundantly although they are very efficient in answering queries on topological adjacency relationships. Storage requirement can arise as a crucial problem in models in which topological data is more dominant than geometric data, such as tessellated or mesh models.
To solve this problem, in this paper, we propose a compact non-manifold boundary representation, called the partial entity structure, which allows the reduction of the storage size to half that of the radial edge structure, which is known as a time efficient non-manifold data structure, while allowing full topological adjacency relationships to be derived without loss of efficiency. This representation contains not only the conventional primitive entities like the region, face, edge, and vertex, but also the partial topological entities such as the partial-face, partial-edge, and partial-vertex for describing non-manifold conditions at vertices, edges, and faces. In order to verify the time and storage efficiency of the partial entity structure, the time complexity of basic query procedures and the storage requirement for typical geometric models are derived and compared with those of existing schemes. Furthermore, a set of the generalized Euler operators and typical high-level modeling capabilities such as Boolean operations are also implemented to confirm that our data structure is sound and easy to be manipulated.
Blood pressure estimation from pulse wave velocity measured on the chest
(2013)

Recently, monitoring of blood pressure fluctuation in the daily life is focused on in the hypertension care area to predict the risk of cardiovascular and cerebrovascular disease events. In this paper, in order to propose an alternative system to the existed ambulatory blood pressure monitoring (ABPM) sphygmomanometer, we have developed a prototype of small wearable device consisting of electrocardiogram (ECG) and photopelthysmograph (PPG) sensors. In addition, it was examined whether blood pressure can be estimated based on pulse wave transit time (PWTT) only by attaching that device on the surface of the chest. We indicated that our system could also sense tendency of time-dependent change of blood pressure by measuring pulse of vessel over the sternum while its propagation distance is short.
School burnout and engagement in the context of demands-resources model.
(2014)

BACKGROUND
A four-wave longitudinal study tested the demands-resources model in the school context.


AIM
To examine the applicability of the demands-resources to the school context.


METHOD
Data of 1,709 adolescents were gathered, once during the transition from comprehensive to post-comprehensive education, twice during post-comprehensive education, and once 2 years later.


RESULTS
The hypotheses were supported, path analysis showing that study demands were related to school burnout a year later, while study resources were related to schoolwork engagement. Self-efficacy was positively related to engagement and negatively to burnout. School burnout predicted schoolwork engagement negatively 1 year later. Engagement was positively related to life satisfaction 2 years later, while burnout was related to depressive symptoms. Finally, burnout mediated the relationship between study demands and mental health outcomes.


CONCLUSIONS
The demands-resources model can usefully be applied to the school context, including the associations between school-related burnout and engagement among adolescents. The model comprises two processes, the energy-depleting process and the motivational process.
Search , Obfuscation , and Price Elasticities on the Internet 1
(2001)

We examine the competition between a group of Internet retailers who operate in an environment where a price search engine plays a dominant role. We show that for some products in this environment, the easy price search makes demand tremendously pricesensitive. Retailers, though, engage in obfuscation—practices that frustrate consumer search or make it less damaging to firms—resulting in much less price sensitivity on some other products. We discuss several models of obfuscation and examine its effects on demand and markups empirically.
Cost-aware depth map estimation for Lytro camera
(2014)

Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.
OpenFlow: enabling innovation in campus networks
(2008)

This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use every day. OpenFlow is based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries. Our goal is to encourage networking vendors to add OpenFlow to their switch products for deployment in college campus backbones and wiring closets. We believe that OpenFlow is a pragmatic compromise: on one hand, it allows researchers to run experiments on heterogeneous switches in a uniform way at line-rate and with high port-density; while on the other hand, vendors do not need to expose the internal workings of their switches. In addition to allowing researchers to evaluate their ideas in real-world traffic settings, OpenFlow could serve as a useful campus component in proposed large-scale testbeds like GENI. Two buildings at Stanford University will soon run OpenFlow networks, using commercial Ethernet switches and routers. We will work to encourage deployment at other schools; and We encourage you to consider deploying OpenFlow in your university network too
Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review
(2016)

With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.
Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications
(2015)

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.
Maestro: A System for Scalable OpenFlow Control
(2010)

The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization
A Survey of Fog Computing: Concepts, Applications and Issues
(2015)

Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.
Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues
(2010)

This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.
The raven roosting optimisation algorithm
(2016)

A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.
Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations.
(1999)

A system for quantifying the physiological features of emotional stress is being developed for use during a driving task. Two prototypes, using sensors that measure the driver's skin conductance, respiration, muscle activity, and heart activity are presented. The first system allows sampling rates of 200 Hz on two fast channels and 20 Hz on six additional channels. It uses a wearable computer to do real-time processing on the signals and has an attached digital camera which was used to capture images of the driver's facial expression once every minute. The second system uses a car-based computer that allows a sampling rate of 1984 samples per second on eight channels. This system uses multiple video cameras to continuously capture the driver's facial expression and road conditions. The data is then synchronized with the physiological signals using a video quad-splitter. The methods for extracting physiological features in the driving environment are discussed, including measurement of the skin conductance orienting response, muscle activity, pulse, and respiration patterns. Preliminary studies show how using multiple modalities of sensors can help discriminate reactions to driving events and how individual's response to similar driving conditions can vary from day to day.
SOCIAL SPACE AND SYMBOLIC POWER *
(2007)

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A customizable pipeline for social media text normalization
(2017)

Social networks are persistently generating text-based data that encapsulate vast amounts of knowledge. However, the presence of non-standard terms and misspellings in texts originating from social networks poses a crucial challenge for natural language processing and machine learning systems that attempt to mine this knowledge. To address this problem, we propose a sequential, modular, and hybrid pipeline for social media text normalization. In the first phase, text preprocessing techniques and social media-specific vocabularies gathered from publicly available sources are used to transform, with high precision, out-of-vocabulary terms into in-vocabulary terms. A sequential language model, generated using the partially normalized texts from the first phase, is then utilized to normalize short, high-frequency, ambiguous terms. A supervised learning module is employed to normalize terms based on a manually annotated training corpus. Finally, a tunable, distributed language model-based backoff module at the end of the pipeline enables further customization of the system to specific domains of text. We performed intrinsic evaluations of the system on a publicly available domain-independent dataset from Twitter, and our system obtained an F-score of 0.836, outperforming other benchmark systems for the task. We further performed brief, task-oriented evaluations of the system to illustrate the customizability of the system to domain-specific tasks and the effects of normalization on downstream applications. The modular design enables the easy customization of the system to distinct types domain-specific social media text, in addition to its off-the-shelf application to generic social media text.
Friend or Foe: Cyberbullying in Social Network Sites
(2016)

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.
Face Recognition: Features Versus Templates
(1993)

Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets (about 90% correct recognition using geometrical features and perfect recognition using template matching) favor our implementation of the template-matching approach.
QR Code Security: A Survey of Attacks and Challenges for Usable Security
(2014)

QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.
Additive Manufacturing Defect Detection using Neural Networks
(2016)

Currently defect detection in additive manufacturing is predominately done by traditional image processing, approximation, and statistical methods. Two important aspects of defect detection are edge detection and porosity detection. Both problems appear to be good candidates to apply machine learning. This project describes the implementation of neural networks as an alternative method for defect detection. Results from current methods and the neural networks are compared for both speed and accuracy.
Secure and light IoT protocol (SLIP) for anti-hacking
(2017)

In the elemental technologies, it is necessary to realize the Internet service of things (IoT), sensors and devices, network, platform (hardware platforms, open software platform, such as specific OS platforms). Web services, data analysis and prediction, big data processing, such as security and privacy protection technology, there are a variety of techniques. These elements technology provide a specific function. The element technology is integrated with each other. However, by several techniques are integrated, it can be problems with integration of security technologies that existed for each element technology. Even if individual technologies basic security features are constituting Internet Services (CIA: Confidentiality, integrity, authentication or authorization). It also offers security technology not connected to each other. Therefore, I will look at the security technology and proposes a lightweight routing protocol indispensable for realizing a secure Internet services things.